Explainable Unsupervised and Semi-Supervised Learning Based Animal Tracking Assistant for Dynamic Environments

Abstract

Target tracking in dynamic environments is very challenging because the variability in target motion, background clutter, and lighting conditions vary. Classic tracking models are not adaptive and interpretable in real time due to their complex nature. Here, the project work is for overcoming such limitations by proposing an explainable, semi-supervised, and unsupervised learning framework which improves the tracking accuracy with a clear idea about model interpretability. The integrated approach merges unsupervised clustering (CLIP-based feature extraction with KMeans) and semi-supervised fine-tuning of a supervised classifier on diverse pre-processed datasets. Motion and context inform data pre-processing through noise reduction, data augmentation, and meaningful CLIP feature extraction. Furthermore, framework employs SHAP as an explainability technique to reveal individual feature contributions to tracking decisions. To enhance robustness and adaptability to novel environmental conditions, incremental fine-tuning via selective unfreezing of CLIP encoder layers and end-to-end retraining is incorporated. The system is evaluated quantitatively by means of Average Precision (mAP) and qualitatively by inference speed (FPS), demonstrating competitive performance and interpretability.